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   "source": [
    "# Introduction\n",
    "\n",
    "This first exercise is based on the python crashcourse offered by the department \"Nachrichtentechnik\" and \"Elektrische Messtechnik\".\n",
    "\n",
    "[Python Crash-Course](https://fgnt.github.io/python_crashkurs/#/)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
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     "grade_id": "cell-1fecd7143400f744",
     "locked": true,
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   },
   "source": [
    "## MATLAB vs. Python\n",
    "\n",
    "|                             MATLAB                             |                                       Python                                       |\n",
    "|:--------------------------------------------------------------:|:----------------------------------------------------------------------------------:|\n",
    "|                           Commercial                           |                                    Open Source                                     |\n",
    "|     New functions via MATLAB Toolkits<br>(no package manager)     |        Installation of new modules with<br>package manager (conda or pip)         |\n",
    "| Mainly procedual programming<br>(Objects exists but are a hassle) |                                  Object oriented                                   |\n",
    "|               Mathematical Programming Language                |             General Purpose Language with<br>many mathematical modules              |\n",
    "|                No Namespaces for Core-Functions                |               Proper Namespaces (e.g. `plt.plot` instead of `plot`)                |\n",
    "|                          GUI included                          | Various GUIs available.<br>We recommend [vscode](https://code.visualstudio.com/) |\n",
    "|   Download: [Mathworks](https://de.mathworks.com/downloads/)   |              Download: [Anaconda](https://www.anaconda.com/download/)              |\n",
    "\n",
    "\n",
    "### Numpy for MATLAB users\n",
    "[https://docs.scipy.org/doc/numpy-1.15.0/user/numpy-for-matlab-users.html](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html)\n",
    "\n",
    "## Common Libraries\n",
    "* Numpy (Vector and Matrix operations, Numeric computing)\n",
    "* Matplotlib (Plotting)\n",
    "* Pandas/Polars (Table operations)\n",
    "* Scikit-Learn (Machine Learning)\n",
    "* Tensorflow / PyTorch (Neural Networks)\n",
    "* SymPy (Symbolic computations)\n",
    "* Seaborn (Alternative Plotting)\n",
    "* ...\n",
    "\n",
    "## Quickstart\n"
   ]
  },
  {
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   "execution_count": 2,
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   "outputs": [
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "U_0 = 3     # V\n",
    "u_peak = 2  # V\n",
    "f_0 = 50    # 1/s\n",
    "\n",
    "# Timevector in s (Sequence of numbers)\n",
    "t = np.arange(start=0, stop=0.04, step=0.001)\n",
    "u = U_0 + u_peak * np.sin(2 * np.pi * f_0 * t)\n",
    "plt.plot(t, u, 'o--')\n",
    "plt.xlabel('Time $t$ / s')\n",
    "plt.ylabel('Voltage $u(t)$ / V')\n",
    "plt.grid(True)\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Create Assignment",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
